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videocount.py

+import cv2
+from yolov5processor.infer import ExecuteInference
+import glob
+import numpy as np
+#from google.colab.patches import cv2_imshow
+
+yp = ExecuteInference(weight="E:\buds\best.pt", confidence=0.75, iou= 0.45, agnostic_nms= False, img_size= 416)
+#yp = ExecuteInference(weight=r"E:\buds\best.pt")
+vid = cv2.VideoCapture("E:\buds\original video\predict.mp4")
+frame_width = int(vid.get(3))
+frame_height = int(vid.get(4))
+
+
+#count_line_position=550
+detect = []
+offset=6
+while (True):
+    faulty_count = 0
+    good_count = 0
+    rejected_count = 0
+    ret, image = vid.read()
+
+    pred = yp.predict(image)
+    print(pred)
+    for det in pred:
+        conf = "{:.2f}".format(int(det['conf'] * 100))
+        if int(det['conf'] * 100) > 50:
+        # print(det['class'])
+           label = det['class']  # print(label.item())
+           det = det['points']
+           if label == "faulty":
+               cv2.rectangle(image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), (0, 0, 255), 2)
+
+               cv2.putText(img=image, text=conf + label, org=(int(det[0]), int(det[1]) - 10), color=(0, 0, 225),
+                        thickness=3,
+                        fontScale=1, fontFace=cv2.LINE_AA)
+               faulty_count += 1
+           elif label =="good" :
+                cv2.rectangle(image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), (0, 0, 255), 2)
+
+                cv2.putText(img=image, text=conf + label, org=(int(det[0]), int(det[1]) - 10), color=(0, 0, 225),
+                         thickness=3,
+                        fontScale=1, fontFace=cv2.LINE_AA)
+                good_count += 1
+                    
+           else :
+                cv2.rectangle(image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), (0, 0, 255), 2)
+
+                cv2.putText(img=image, text=conf + label, org=(int(det[0]), int(det[1]) - 10), color=(0, 0, 225),
+                            thickness=3,
+                            fontScale=1, fontFace=cv2.LINE_AA)
+                rejected_count += 1
+                
+        print(f"good-{good_count}, faulty-{faulty_count}, rejected-{rejected_count}")
+    
+        
+    
+            
+        print("good:",good_count)
+        print("faulty:",faulty_count)
+        print("rejected:",rejected_count)
+      
+    result = f"Good: {good_count}, Faulty: {faulty_count}, Rejected: {rejected_count}"
+    
+    cv2.putText(image,result,(450,70),cv2.FONT_HERSHEY_SIMPLEX,2,(0,0,255),5)
+           
+            
+    
+
+    cv2.imshow('frame', cv2.resize(image, (720, 560)))
+    #cv2_imshow(cv2.resize(image, (720, 560)))
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+      break
+    
+
+# After the loop release the cap object
+vid.release()
+# Destroy all the windowsss
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